A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers

Telehealthcare for chronic obstructive pulmonary disease

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a disease of irreversible airways obstruction in which patients often suffer exacerbations. Sometimes these exacerbations need hospital care: telehealthcare has the potential to reduce admission to hospital when used to administer care to the pateint from within their own home. OBJECTIVES: To review the effectiveness of telehealthcare for COPD compared with usual face‐to‐face care. SEARCH METHODS: We searched the Cochrane Airways Group Specialised Register, which is derived from systematic searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL, AMED, and PsycINFO; last searched January 2010. SELECTION CRITERIA: We selected randomised controlled trials which assessed telehealthcare, defined as follows: healthcare at a distance, involving the communication of data from the patient to the health carer, usually a doctor or nurse, who then processes the information and responds with feedback regarding the management of the illness. The primary outcomes considered were: number of exacerbations, quality of life as recorded by the St George's Respiratory Questionnaire, hospitalisations, emergency department visits and deaths. DATA COLLECTION AND ANALYSIS: Two authors independently selected trials for inclusion and extracted data. We combined data into forest plots using fixed‐effects modelling as heterogeneity was low (I(2) < 40%). MAIN RESULTS: Ten trials met the inclusion criteria. Telehealthcare was assessed as part of a complex intervention, including nurse case management and other interventions. Telehealthcare was associated with a clinically significant increase in quality of life in two trials with 253 participants (mean difference ‐6.57 (95% confidence interval (CI) ‐13.62 to 0.48); minimum clinically significant difference is a change of ‐4.0), but the confidence interval was wide. Telehealthcare showed a significant reduction in the number of patients with one or more emergency department attendances over 12 months; odds ratio (OR) 0.27 (95% CI 0.11 to 0.66) in three trials with 449 participants, and the OR of having one or more admissions to hospital over 12 months was 0.46 (95% CI 0.33 to 0.65) in six trials with 604 participants. There was no significant difference in the OR for deaths over 12 months for the telehealthcare group as compared to the usual care group in three trials with 503 participants; OR 1.05 (95% CI 0.63 to 1.75). AUTHORS' CONCLUSIONS: Telehealthcare in COPD appears to have a possible impact on the quality of life of patients and the number of times patients attend the emergency department and the hospital. However, further research is needed to clarify precisely its role since the trials included telehealthcare as part of more complex packages

Heart Disease Detection using Vision-Based Transformer Models from ECG Images

Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Doctor of Philosophy

dissertationDisease-specific ontologies, designed to structure and represent the medical knowledge about disease etiology, diagnosis, treatment, and prognosis, are essential for many advanced applications, such as predictive modeling, cohort identification, and clinical decision support. However, manually building disease-specific ontologies is very labor-intensive, especially in the process of knowledge acquisition. On the other hand, medical knowledge has been documented in a variety of biomedical knowledge resources, such as textbook, clinical guidelines, research articles, and clinical data repositories, which offers a great opportunity for an automated knowledge acquisition. In this dissertation, we aim to facilitate the large-scale development of disease-specific ontologies through automated extraction of disease-specific vocabularies from existing biomedical knowledge resources. Three separate studies presented in this dissertation explored both manual and automated vocabulary extraction. The first study addresses the question of whether disease-specific reference vocabularies derived from manual concept acquisition can achieve a near-saturated coverage (or near the greatest possible amount of disease-pertinent concepts) by using a small number of literature sources. Using a general-purpose, manual acquisition approach we developed, this study concludes that a small number of expert-curated biomedical literature resources can prove sufficient for acquiring near-saturated disease-specific vocabularies. The second and third studies introduce automated techniques for extracting disease-specific vocabularies from both MEDLINE citations (title and abstract) and a clinical data repository. In the second study, we developed and assessed a pipeline-based system which extracts disease-specific treatments from PubMed citations. The system has achieved a mean precision of 0.8 for the top 100 extracted treatment concepts. In the third study, we applied classification models to reduce irrelevant disease-concepts associations extracted from MEDLINE citations and electronic medical records. This study suggested the combination of measures of relevance from disparate sources to improve the identification of true-relevant concepts through classification and also demonstrated the generalizability of the studied classification model to new diseases. With the studies, we concluded that existing biomedical knowledge resources are valuable sources for extracting disease-concept associations, from which classification based on statistical measures of relevance could assist a semi-automated generation of disease-specific vocabularies

Doctor of Philosophy

DissertationHealth information technology (HIT) in conjunction with quality improvement (QI) methodologies can promote higher quality care at lower costs. Unfortunately, most inpatient hospital settings have been slow to adopt HIT and QI methodologies. Successful adoption requires close attention to workflow. Workflow is the sequence of tasks, processes, and the set of people or resources needed for those tasks that are necessary to accomplish a given goal. Assessing the impact on workflow is an important component of determining whether a HIT implementation will be successful, but little research has been conducted on the impact of eMeasure (electronic performance measure) implementation on workflow. One solution to addressing implementation challenges such as the lack of attention to workflow is an implementation toolkit. An implementation toolkit is an assembly of instruments such as checklists, forms, and planning documents. We developed an initial eMeasure Implementation Toolkit for the heart failure (HF) eMeasure to allow QI and information technology (IT) professionals and their team to assess the impact of implementation on workflow. During the development phase of the toolkit, we undertook a literature review to determine the components of the toolkit. We conducted stakeholder interviews with HIT and QI key informants and subject matter experts (SMEs) at the US Department of Veteran Affairs (VA). Key informants provided a broad understanding about the context of workflow during eMeasure implementation. Based on snowball sampling, we also interviewed other SMEs based on the recommendations of the key informants who suggested tools and provided information essential to the toolkit development. The second phase involved evaluation of the toolkit for relevance and clarity, by experts in non-VA settings. The experts evaluated the sections of the toolkit that contained the tools, via a survey. The final toolkit provides a distinct set of resources and tools, which were iteratively developed during the research and available to users in a single source document. The research methodology provided a strong unified overarching implementation framework in the form of the Promoting Action on Research Implementation in Health Services (PARIHS) model in combination with a sociotechnical model of HIT that strengthened the overall design of the study

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table